Method and device for fast processing of measurement data with a plurality of independent random samples

ABSTRACT

In a method as well as a device to process measurement data that are composed of a number of data sets with a number of independent random samples originating via temporally successive measurements, for a comparison of the time curve of each acquired random sample with the time curve of a model function using the general linear model, the required calculations are implemented data set-by-data set in a series of the data sets originating from the temporal sequence of measurements, and stored as an intermediate result, with the intermediate results of the directly preceding data set being updated with the new calculations. The comparison can be calculated efficiently and quickly, with a saving of storage space.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention concerns a method as well as a device toprocess measurement data formed by data sets with a number ofindependent random samples originating from temporally successivemeasurements, wherein for each independent random sample comprised inthe data set, a measured time curve (that arises from the measurementdata of the random sample) is compared using the general linear modelwith the time curve of at least one model function comprised in a modelmatrix, in order to test the incidence of specific patterns in thesignal curve.

[0003] More particularly, the present invention concerns the processingof measurement data that are acquired from a subject volume with ameasurement method of functional Imaging, in particular with functionalmagnetic resonance tomography (fMRI), and that are composed of volumedata sets originating from a number of temporally successivemeasurements, with, for each volume element acquired in the subjectvolume, a temporal signal curve that arises from measurement data of thevolume element being compared with the time curve of at least one modelfunction.

[0004] 2. Description of the Prior Art

[0005] In particular in the field of medical technology and medicalresearch, a need exists to acquire information about the brain activityin human and animal organs. The neural activity causes an increase ofthe blood flow in active brain areas, resulting in a decrease in theblood-deoxyhemoglobin concentration. Deoxyhemoglobin is a paramagneticmaterial that reduces the magnetic field homogeneity and therefore canbe shown with the aid of magnetic resonance techniques, since itaccelerates the T₂* signal relaxation.

[0006] Localization of brain activity is enabled by the use of afunctional imaging method that measures the change of the MR signalrelaxation with a time delay. The biological effective mechanism isknown in the literature by the name BOLD effect (Blood Oxygen LevelDependent effect).

[0007] A fast magnetic resonance imaging enables the examination of thebold effect in vivo, dependent on activation states of the brain. Infunctional magnetic resonance tomography, magnetic resonance exposuresof the subject volume to be examined, for example of the brain of apatient, are made in short temporal intervals. By comparison of thesignal curve (measured by functional imaging) for each volume element ofthe subject volume with the time curve of a model function, astimulus-specific neural activation can be detected and spatiallylocalized. A stimulus can be, for example, a somatic sensory, acoustic,visual or olfactory stimulus, as well as a mental or motor task. Themodel function or the model time series specifies the expected signalchange of the magnetic resonance signal in the course of neuralactivation. By using faster magnetic resonance techniques, such as, forexample, the echo-planar method, smaller temporal intervals between theindividual measurements can be realized.

[0008] In many multivariate statistical analyses, a model known as thegeneral linear model (GLM) is used for the comparison of the measuredsignal curve with the time curve of a model function. With the generallinear model, it is determined which linear combination of the modelfunctions best approximates the measurement data series. Furthermore, itcan be calculated for each model function how significantly themeasurement data of the null hypothesis of no contribution of therespective model function contradicts the measurement data series. Thegeneral linear model is used in many fields, such as, for example,physics and sociology, to analyze measurement data. It is in particularalso suitable to analyze time series as they are measured in functionalmagnetic resonance imaging (fMRI). By using the general linear model, itcan be analyzed whether the measured time series show a pattern thatcorresponds to local neural activity. In addition to this pattern,however, the time series frequently also show other characteristics(such as, for example, drifts or other effects) that can likewise bemodeled in the framework of the general linear model. This enables abetter analysis of the measurement data than, for example, a t-test orcorrelation method. Thus, for example, it is also possible with thegeneral linear model to analyze in parallel a number of effects in thebrain. Group statistics about a number of test persons are alsopossible. Further application possibilities of the general linear modelare found, for example, in “Human Brain Function” by R. Frackowiak etal., Academic Press.

[0009] In the processing of measurement data that are acquired from asubject volume with the method of functional magnetic resonancetomography, it has been necessary until now to load in the main storageof a computer the overall measurement data that comprises a number ofvolume data sets originating via temporally successive measurements.Subsequently, for each volume element of the measured subject volume,the signal curve or, respectively, the time series must be extractedfrom these measurement data and be compared with the model function. Inthe known Implementation of the general linear model in the freelyavailable SPM software (Wellcome Department of Cognitive Neurology;University of London; published under Gnu Public License;http://www.fil.ion.ucl.ac.uk/spm/), it is likewise necessary to loadinto the main storage of the computer the complete data set to beanalyzed. The complete data set, in long fMRI studies—possibly alsospanning a number of test persons—can include several hundred megabytes,up to gigabytes, of data. The values to be analyzed that belong to atime series of measurement data are extracted, and the general linearmodel is directly calculated.

[0010] This conventional data processing therefore leads to asignificant main storage requirement. Since the measurement datatypically exist in the storage of the computer volume by volume,corresponding to the successive measurements as a number of volume datasets, the use of this known technique also leads to very long computingtimes, since the individual time series must be collected together oververy large ranges of the loaded data.

[0011] Presorting of the data could reduce this computing time, howeverthis requires in turn a considerable computing time and additionalstorage requirements. Moreover, a sorting event can not be finisheduntil the end of the measurement, since the time series only then existsin full. The actual calculations thus can be started only at the end ofthe measurement.

SUMMARY OF THE INVENTION

[0012] An object of the invention is to provide a method and a devicefor fast and storage-saving processing of measurement data that areformed of a number of data sets with a plurality of independent randomsamples, originating from temporally successive measurements, The methodshould in particular enable fast and storage-saving calculation of thegeneral linear model for the case of a number of independent randomsamples, for example measurement data series as they occur in fMRI.

[0013] This object is achieved in accordance with the invention for theprocessing of measurement data that is composed of a number of data setswith a number of independent random samples, originating from temporallysuccessive measurements.

[0014] In the following, the method and its advantages are explained(without limitation to other applications) using the processing ofmeasurement data that are acquired from a subject volume with ameasurement method of functional imaging, in particular with the methodof functional magnetic resonance tomography, and that are formed of anumber of volume data sets originating from temporally successivemeasurements. In this method, for each volume element acquired in thesubject volume, a temporal signal curve is determined from themeasurement data of this volume element, and at least one model functionobtained in a model matrix G using the general linear model is comparedwith the time curve in order to test the incidence of specificcharacteristics or patterns in the signal curve. The present method isprimarily distinguished in that calculations necessary for thecomparison from the measurement data in a sequence of the of the datasets, or volume data sets originating from the time sequence of themeasurements, are implemented for all relevant measurement data of avolume data set and are stored as intermediate results. The intermediateresults from the directly preceding volume data set are updated with thenew calculations, such that at any time intermediate results can becalculated, and after the one-time cycle of all volume data sets an endresult exists that allows an assertion about the incidence of thecharacteristics or patterns in the signal curve. The square of an errorvector necessary for the end result Is obtained from the difference ofthe square of a measurement value vector formed from the measurementdata and the square of a model vector calculated from the modelfunction.

[0015] In contrast to the known method of the prior art, in the presentmethod the measurement data are not cycled for the comparison accordingto time series or volume elements, but rather volume-by-volume. Afterimplementation of the calculations with the measurement data and storageof an intermediate result, each volume data set can again be discardedfrom the main storage of the data processing system. For this reason, itis no longer necessary to load the entirety of the measurement data,meaning all volume data sets of the measurements, into the workingstorage of the data processing system. Rather, it is sufficient merelyto load the measurement data of an individual volume data set. For thisreason, the present method can be implemented efficiently and quickly,saving much storage space, since respectively only small data rangesmust be accessed in the calculation. The volume data sets must herebyoverall only be cycled once, volume-by-volume.

[0016] In the solution of the above object, consideration has been madethat the present computer architecture is very slow in the calculationof large data ranges, in particular in the gigabyte range, however itcan implement small data ranges with noticeably higher speed. With thepresent method, in which only small data ranges are always used forcalculation volume-by-volume, a noticeably higher calculation speed thuscan be achieved. Furthermore, the storage requirement that is necessaryfor the processing of the data is no longer dependent in the presentmethod on the number of the volume data sets (meaning the number of themeasurements), but rather is substantially dependent only on the size ofthe measured subject volume. A further advantage of the present methodis that all calculations can already be implemented during themeasurement. Real-time applications as well as the display of analways-current Intermediate result are also thereby possible.Furthermore, the model function in the present method can first bedefined during the measurement, and does not have to be known a priori.The method can be implemented in parallel very efficiently, such that anumber of computers or processors can be used to accelerate thecalculations.

[0017] The present method is explained again as an example using an fMRIdata set, however it can naturally also be applied in the same manner todata sets of other functional imaging methods or other measurement datawith independent random samples, corresponding to the measurement dataseries of the individual volume elements in the fMRI.

[0018] In functional magnetic resonance tomography, the subject volume,for example the brain of a test person, is repeatedly sampled threedimensionally at small temporal intervals, and from the raw data thedesired image information is reconstructed in voxels, meaning volumeelements, via a Fourier transformation. A corresponding measurementvalue is hereby associated with each volume element. The overallmeasurement is composed of a number of volume data sets that form themeasurement data of the measurements temporally following in quicksuccession. Each volume data set is composed of the measurement data,acquired at a specific point in time, of the individual voxel of thesubject volume. For the recognition of activation states of the measuredbrain, the signal curves resulting from the temporally successivemeasurement values of each voxel are compared in order to be able toestablish the degree of a coincidence. Due to the use the widespreadgeneral linear model (GLM) according to the present method, thiscomparison can be implemented with a great saving of storage space. Inthe general linear model, a design matrix or model matrix G is generatedwith one or more model functions from which, in connection with themeasurement data of each volume element, values (in particular at-statistic) can be calculated, from which the degree of coincidencewith the model functions of the model matrix is visible.

[0019] In the implementation of the method using the general linearmodel, the volume data sets of the measurements are only cycled once.After cycling each data set with the thereby-implemented calculations,an intermediate result is stored that is updated upon cycling of thedirectly ensuing volume data set via the new calculations—due to the newmeasurement data—and is stored as an intermediate result of this newvolume data set. By this volume-by-volume updating of the intermediateresults, it is possible to obtain a result at any time from which, forexample, the t-statistic can be calculated. The square of the errorvector required for this is obtained from the difference of the squareof the measurement value vector formed from the measurement data and thesquare of the model vector calculated from the model function. Thissimple calculation is possible since the error vector and the modelvector are perpendicular to one another, due to the least squarescondition, and when added yield the measurement value vector. For thecalculation of the square of the error vector, therefore only the lengthof the measurement value vector and the length of the model vector mustbe determined. The square of the error vector can then be calculated bythe set of Pythagoras. The model vector thereby comprises the valuescalculated from the model function that should approach as best possiblethe measurement values.

[0020] The present method is thus based on an update event in which thevolume data sets are only cycled once. At the end of the update event,the data necessary for the calculation of the t-statistic from themeasurement values are all available.

[0021] In one embodiment, the update event, the cycle through the volumedata sets, can ensue simultaneously with the measurement, with therespective calculation being implemented directly after obtaining a newvolume data set. In this embodiment, the update event is thereforealready finished with the end of the data acquisition.

[0022] Given sufficient computing speed, a real-time application of thepresent method is also possible, in that during the measurement regularintermediate evaluations are undertaken in which the update event isinterrupted and the calculation of the t-statistic is implemented. Theupdate event can subsequently be continued for the volume data setsmeasured in the following time period.

[0023] In the present method, buffers are necessary that, for the use ofthe general linear model, must exhibit a size that satisfy the followingequation:

[0024] Size of the storage=number of the volume elements×(2×number ofthe model functions+2)×4 bytes.

[0025] The four bytes as a storage space for a floating-point number ofthe float data type have turned out to be sufficient for theimplementation of the present method. The number of the volume elementscan, for example, comprise 64×64×32 values. This corresponds to the sizeof a typical sample data set of a subject volume in functional magneticresonance tomography.

[0026] The present method can be used, for example, in a computer of amagnetic resonance scanner, such that a fast post-processing of fMRIdata is possible. Via suitable implementation of the method, therepresentation of the comparison results can also ensue in real-time, aslong as the data sets do not comprise too many volume elements and thenumber of the model functions is not chosen too high. In particular, thecalculation in real-time is not limited by the number of the volume datasets, since the calculation time is constant, independent of the numberof the already added data sets.

DESCRIPTION OF THE DRAWINGS

[0027] The FIGURE is a flow chart of an exemplary embodiment of theinventive method.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0028] In the inventive method, first the model matrix G is defined,that is composed of a number of columns with model functions. The timeseries in the present invention merely contains twenty measurements. Foreach volume element, twenty measurement values x, are thereby obtained,whereby m=0 . . . max−1 and max=20. As a model matrix, a matrix withfive model functions is selected, meaning a matrix with five columns andtwenty rows.

[0029] In the first column is the model function of the neuralactivation that for example can be formed by:

G=(11111000110011001100)^(T)

[0030] In the second column, a constant ration is represented in themeasurement data:

G _(M,1)=1.

[0031] The further three columns serve as high-pass filters, meaningthey model slow drifts during the measurement on a cosine basis [cosinusbasis]. These model functions can, for example, be selected as$\begin{matrix}{G_{m,2} = {\cos \left( \frac{\pi \cdot i}{\max} \right)}} & {G_{m,3} = {\cos \left( \frac{2 \cdot \pi \cdot i}{\max} \right)}} & {G_{m,4} = {\cos \left( \frac{3 \cdot \pi \cdot i}{\max} \right)}}\end{matrix}$

[0032] The model matrix G_(mj) thus comprises the collection of modelfunctions that should be adapted to the measurement data x_(m).

[0033] In the implementation of the comparison of the measurement datawith these model functions, a least-squares estimate of the parametersis implemented. A vector b is thereby defined, from which thecoincidence of the measurement data series with individual modelfunctions of the model matrix is visible. This vector b is calculated inthe following manner:

B=(G ^(T) ·G)⁻¹ ·G ^(T) ·x.

[0034] The vector b in the present example is composed of five values,corresponding to the number of the columns or model functions of themodel matrix. Each value represents how the corresponding model functionmust be scaled in order to approximate the measurement data.

[0035] Next, an error consideration must be implemented in order toobtain an assertion about the quality of the estimate. For this, thevalue σ is calculated that results in the following manner:$\sigma:=\sqrt{\frac{ \cdot }{\max - {{rang}(G)}}}$

[0036] whereby e=G·b−x.

[0037] Finally, what is known, as the t-statistic is calculated by, foreach volume element, obtaining a value t having a magnitude that is ameasure for the degree of the coincidence of the measurement data seriesrepresented with the considered model function. In functional imagingmethods, such as functional magnetic resonance tomography explained hereas an example, it is typical to visualize these t-values. For thecalculation of the t-statistic, a contrast must first be defined thatspecifies the model function of interest. In the present case, thiscontrast c can be given by the following value: c=(10000)^(T).

[0038] The t-value then results in the following manner:$t:=\frac{c \cdot b}{\sqrt{\sigma^{2} \cdot c \cdot \left\lbrack {\left( {G^{T} \cdot G} \right)^{- 1} \cdot c} \right\rbrack}}$

[0039] In the implementations implemented until now in the prior art,the time series of each voxel is extracted in succession from the fMRIdata set and undergoes the analysis above. However, for this it isnecessary for the calculation to simultaneously load all measurementdata of all volume data sets into the working storage of a computer. Inaddition to the high storage space requirement, this also leads to acorrespondingly slow calculation speed.

[0040] In contrast to this, with the present invention as shown in theFIGURE in an embodiment, both the storage space requirement can bereduced and the calculation speed can be increased. In the inventivemethod, in the update event the fMRI data set is cycled volume byvolume, meaning one volume data set after the other, and the respectivecalculations are implemented in each volume data set for each volumeelement.

[0041] This can ensue in the following manner. In the update event, inwhich all volume data sets are cycled once, the vector GTx is updatedfor each new volume data set and each voxel, whereby respectively onlythe new line of the model matrix and the now measurement value areaccessed:

GTx _(l) =GTx _(i) +G _(m,l) ·x _(m)

[0042] GTX hereby designates the vector G^(T)·x. This calculation isimplemented in each new volume data set for each volume element, and canalso be distributed voxel by voxel onto various computers or,respectively, processors. The value obtained from the preceding volumedata set for the Individual components of the vector GTx is herebyupdated. Of course, this value must be set to zero in the implementationof the calculation for the very first volume data set.

[0043] The implementation of this calculation for each volume data setimplies that a sufficient buffer must be present for the respectiveinterim value of GTx that corresponding to the number of the volumeelements in the volume data set multiplied by the number of the modelfunctions in the model matrix.

[0044] Furthermore, in this cycle of the data sets for each new volumedata set and each voxel, the square XX of the measurement value vectorus updated, with only the new measurement value is accessed:

XX=XX+(x _(m))²

[0045] At the same time, in this cycle the matrix GTG (corresponds toG^(T)·G) can also be update by following instruction:

GTG _(i,k) =GTG _(i,k) +G _(m,l) ·G _(m,k).

[0046] Naturally, this calculation (since it involves no measurementdata) also can be implemented in a step before the cycle, as indicateddashed in the FIGURE, or after this cycle. However, in a model matrixwhose model functions are first defined or changed in the course of themeasurement, or when intermediate results should be calculated, theimplementation is necessary according to the above instruction, meaningthe volume-by-volume calculation during the cycle.

[0047] After obtaining the matrix GTG, this is inverted. This can ensue,for example, by means of an LU decomposition, since this matrix is asmall, symmetric, real matrix, in the present example a 5×5 matrix.Naturally, however, other inversion algorithms can also be used.Finally, the vector b is calculated for each volume element, whichresults by

b=GTG ⁻¹ ·GTx

[0048] After implementation of the update event, the values for thevector b and the pseudo-inverse (G^(T)·G)⁻¹ are thus established.

[0049] To calculate the value for the square of the error vector e(designated in the following as EE), used is made of the fact that thepart of the measurement value vector (designated as model vector M)representable via the model, and the vector E, are perpendicular to oneanother, due to the least squares condition, and when added result inthe measurement value vector X. Thus only the length of the measurementvalue vector and the length of the model vector must be determined inorder to be able to calculate from this the set of Pythagoras EE. Thus:M = G ⋅ b  Model  matrix = M ⋅ M${MM} = {{\left( {G \cdot b} \right) \cdot \left( {G \cdot b} \right)} = {{q \cdot q} = {\sum\limits_{i = 0}^{\max - 1}\quad {q_{i} \cdot q_{i}}}}}$

[0050] with$q_{i} = {\sum\limits_{j = 0}^{{sp} - 1}\quad {G_{i,j} \cdot b_{j}}}$

[0051] The use of which results in:${MM} = {{\sum\limits_{i = 0}^{\max - 1}{\sum\limits_{j = 0}^{{sp} - 1}\quad {G_{i,j} \cdot b_{j} \cdot {\sum\limits_{k = 0}^{{sp} - 1}\quad {G_{i,k} \cdot b_{k}}}}}} = \left\lbrack {\sum\limits_{i = 0}^{\max - 1}\left\lbrack {\sum\limits_{j = 0}^{{sp} - 1}{\sum\limits_{i = 0}^{{sp} - 1}{\left( {G_{i,j},G_{i,k}} \right) \cdot b_{j} \cdot b_{k}}}} \right\rbrack} \right\rbrack}$

[0052] The inner parentheses represent the matrix GTG⁻¹ that was alreadycalculated. Thus;

MM=MM+GTG _(j,k) ·b _(j) ·b _(k)

[0053] such that MM can be calculated quickly, and leads directly to EE:

EE=XX−MM.

[0054] For the previously implemented calculations:

[0055]i=0 . . . sp−1

[0056]k=0 . . . sp−1

[0057]j=0 . . . sp−1

[0058]m=0 . . . max−1

[0059] whereby max corresponds to the number of the measurements or,respectively, volume data sets and sp corresponds to the columns of themodel matrix G.

[0060] After each addition of a volume data set, or also at the end ofthe measurement, the t-statistic cannot be calculated for thiscomparison. In the present example, a scaling factor Scale is herebyfirst calculated according to the following instruction:

Scale=Scale+contrast increase·GTG_(i,j) ·c _(j).

[0061] whereby c corresponds to the contrast vector.

[0062] This calculation is implemented for each volume element.Furthermore, the σ² is calculated for each volume element, which resultsaccording to the following known instruction:$\sigma^{2}:=\frac{UEE}{\max - {{rang}(G)}}$

[0063] From this value, what is known as the t-value can be separatelycalculated for each voxel by

t+c _(l) ·b _(i).

[0064] This t is finally divided again by Eq, in order to obtain foreach voxel the final t-value that, for example, can be visualized bysuperimposition on a typical magnetic resonance exposure of the subjectvolume. The final calculations can likewise by distributed voxel byvoxel to different computers or, respectively, processors calculating inparallel, in order to accelerate the calculation.

[0065] Furthermore, the FIGURE shows the possibility, indicated by thedashed arrows, to calculate and to output an intermediate result duringthe cycle of the data sets.

[0066] Although modifications and changes may be suggested by thoseskilled in the art, it is the intention of the inventor to embody withinthe patent warranted hereon all changes and modifications as reasonablyand properly come within the scope of his contribution to the art.

I claim as my invention:
 1. A method to process measurement datacomprised of a plurality of data sets with a plurality of independentrandom samples originating via temporally successive measurementscomprising the steps of: for each independent random sample in the dataset, comparing a measured time curve arising from measurement data x ofthe random sample, using the general linear model, with a time curve ofat least one model function of a model matrix G, to test for incidenceof specific characteristics in the measured time curve; implementingindividual calculations for the comparison from the measurement data ina sequence of the of the data sets, or volume data sets originating fromthe time sequence of the measurements, for all relevant measurement dataof a volume data set and storing said calculations as intermediateresults; updating the intermediate results from a directly precedingvolume data set with new calculations, such that at any timeintermediate results can be calculated, and after a one-time cycle ofall volume data sets an end result exists from which an assertion aboutthe incidence of the characteristics in the signal curve is derived; andobtaining the square of an error vector for the intermediate or endresult from a difference of the square of a measurement value vectorformed from the measurement data and the square of a model vectorcalculated from the model function.
 2. A method as claimed in claim 1comprising calculating, in the cycle of the data sets, for each data setm and each random sample, vector elements GTx _(i) =GTx _(i) ^(old) +G_(m,i) ·G _(m,k) and the square of the measurement value vector XX=XX^(old)+(x _(m))² from the calculated vector elements GTx_(i) ^(old) andthe square of the vector XX^(old) of the directly preceding data set,and for the subsequent data set and storing said squares as anintermediate result, whereby m=0 . . . max−1, i=0 . . . sp−1, k=0 . . .sp−1, sp corresponds to of the number of the columns of the model matrixG, and max corresponds to the total number of the data sets, and wherebysubsequently the square of the model vector MM=MM ^(old) +GTG _(j,k) ·b_(j) ·b _(k) and the value of the square of the error vector EE=XX−MMare calculated, whereby j=0 . . . sp-1 and b_(j), b_(k) are vectorelements of the vector b=GTG⁻¹·GTx that are calculated in anintermediate step.
 3. A method as claimed in claim 2, comprisingcalculating in the cycle of the data sets, furthermore for each data setm the matrix elements GTG_(i,k) =GTG _(i,k) ^(old) +G _(m,i) ·G _(m,k)from the calculated vector elements GTG_(i,k) ^(old) of the directlypreceding data set, and for the subsequent data set are stored as anintermediate result.
 4. A method as claimed in claim 2, comprisingbefore or after the cycle of the data sets, calculating the matrixelements GTG_(i,k) from the model matrix G in a step.
 5. A method asclaimed in claim 2 comprising, after an end of the cycle, calculating at-value from the calculated values for each random sample.
 6. A methodas claimed in claim 1 comprising for said calculations, loading only onedata set is loaded into a working storage of a data processing system,and after the calculations discarding the loaded data set.
 7. A methodas claimed in claim 1 comprising cycling the data sets in parallel withthe measurements.
 8. A method as claimed in claim 7, comprisinginterrupting the cycle of the data sets after a predeterminable numberof measurements, and present a current result of the measurements, andafter this interruption continuing the cycle.
 9. A method as claimed inclaim 1 comprising acquiring said data sets from a subject volume byfunctional imaging, with the data sets originating from temporallysuccessive measurements representing volume data sets, and with the timecurve for an independent random sample comprised in the data setcorresponding to the temporal signal curve of a volume element acquiredin the subject volume.
 10. A data acquisition and processing arrangementcomprising: a data acquisition device that obtains a plurality of datasets with a plurality of random samples originating in temporallysuccessive measurements; and a processor and a working memory connectedto said processor for processing said data sets by, for each independentrandom sample in the data set, comparing a measured time curve arisingfrom measurement data x of the random sample, using the general linearmodel, with a time curve of at least one model function of a modelmatrix G, to test for incidence of specific characteristics in themeasured time curve, implementing individual calculations for thecomparison from the measurement data in a sequence of the of the datasets, or volume data sets originating from the time sequence of themeasurements, for all relevant measurement data of a volume data set andstoring said calculations as intermediate results, updating theintermediate results from a directly preceding volume data set with newcalculations, such that at any time Intermediate results can becalculated, and after a one-time cycle of all volume data sets an endresult exists from which an assertion about the incidence of thecharacteristics in the signal curve is derived, and obtaining the squareof an error vector for the intermediate or end result from a differenceof the square of a measurement value vector formed from the measurementdata and the square of a model vector calculated from the modelfunction.
 11. A device as claimed in claim 10 wherein said dataacquisition device is a magnetic resonance system.
 12. A device asclaimed in claim 11 wherein said data acquisition device is a functionalmagnetic resonance system.